package net.bwie.realtime.jtp.dws.douyin.log.job;

import com.alibaba.fastjson.JSON;
import net.bwie.realtime.jtp.dws.douyin.log.bean.PageViewBean;
import net.bwie.realtime.jtp.dws.douyin.log.function.PageViewBeanMapFunction;
import net.bwie.realtime.jtp.dws.douyin.log.function.PageViewReportReduceFunction;
import net.bwie.realtime.jtp.dws.douyin.log.function.PageViewReportWindowFunction;
import net.bwie.realtime.jtp.utils.DorisUtil;
import net.bwie.realtime.jtp.utils.KafkaUtil;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import java.time.Duration;

public class JtpTrafficPageViewMinuteWindowDwsJob {
    public static void main(String[] args) throws Exception{
        //1.执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.enableCheckpointing(3000L);
        //2.数据源
        DataStream<String> kafkaDataStream = KafkaUtil.consumerKafka(env, "dwd-traffic-page-log");
        //3.数据转换
        DataStream<String> resultStream = handle(kafkaDataStream);
        //4.数据输出
        DorisUtil.saveToDoris(
                resultStream,"jtp_realtime_report","dws_traffic_page_view_window_report"
        );
        //5.触发执行
        env.execute("JtpTrafficPageViewMinuteWindowDwsJob");
    }

    /*
    对页面浏览日志数据进行汇总计算
     */
    private static DataStream<String> handle(DataStream<String> pagestream) {
        //1.按照mid设备id分组，用于计算uv，使用状态state记录今日是否第1次访问
        KeyedStream<String, String> midStream =
                pagestream.keyBy(json -> JSON.parseObject(json).getJSONObject("common").getString("mid"));

        //2.将流中每条日志数据封装实体类bean对象
        DataStream<PageViewBean> beanStream = midStream.map(new PageViewBeanMapFunction());

        //s3.事件时间字段和水位线
        SingleOutputStreamOperator<PageViewBean> timeStream = beanStream.assignTimestampsAndWatermarks(
                WatermarkStrategy
                        .<PageViewBean>forBoundedOutOfOrderness(Duration.ofSeconds(0))
                        .withTimestampAssigner(new SerializableTimestampAssigner<PageViewBean>() {
                            @Override
                            public long extractTimestamp(PageViewBean element, long recordTimestamp) {
                                return element.getTs();
                            }
                        })
        );

        //s4.分组keyby：ar地区，ba品牌，ch渠道，is_new新老访客
        KeyedStream<PageViewBean, String> keyedStream = timeStream.keyBy(
                bean -> bean.getBrand() + "," + bean.getChannel() + "," + bean.getProvince() + "," + bean.getIsNew()
        );

        //s5.窗口:滚动窗口，窗口大小为1分钟
        WindowedStream<PageViewBean, String, TimeWindow> windowStream = keyedStream.window(
                TumblingEventTimeWindows.of(Time.minutes(1))
        );
        //s6.聚合：对窗口中数据计算
//        SingleOutputStreamOperator<String> reportStream = windowStream.apply(new PageViewWindowFunction());

        //s7.窗口数据聚合计算
        SingleOutputStreamOperator<String> resultStream =
                windowStream.reduce(new PageViewReportReduceFunction(), new PageViewReportWindowFunction());

        return resultStream;
    }
}
